Clustering and Unsupervised Pattern Discovery
Unsupervised learning methods reveal structure in unlabeled datasets. This investigates clustering algorithms such as k-means clustering, DBSCAN, and hierarchical agglomerative clustering. Emphasis is placed on determining grouping quality using measures like the silhouette score. Learners examine similarity through distance metrics and feature relationships. Applications include market segmentation and behavioral analysis. It fosters independent pattern discovery skills.
Unsupervised Learning Focus:
- Clustering algorithms and criteria
- Similarity measures and distance metrics
- Pattern identification without labels
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Clustering and Unsupervised Pattern Discovery Conference Speakers
Recommended Sessions
- Capstone Project
- Classification Algorithms and Decision Systems
- Clustering and Unsupervised Pattern Discovery
- Convolutional Neural Networks (CNNs)
- Data Preparation and Feature Processing
- Deployment of ML Models
- Dimensionality Reduction and Feature Selection
- Ensemble Learning Approaches
- Ethics and Responsible AI
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- Feature Engineering and Representation Design
- Foundations of Machine Learning
- Generative Models and Creativity in AI
- Introduction to Deep Learning
- Model Evaluation and Performance Validation
- Natural Language Processing Basics
- Neural Network Architecture and Optimization
- Optimization and Hyperparameter Tuning
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